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README.md
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<img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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<a href="https://x.com/Agentica_" style="margin: 2px;">
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DeepSWE-Preview is trained on top of Qwen3-32B with thinking mode enabled. With just 200 steps of RL training, SWE-Bench-Verified score increases by ~20%.
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Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](
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<div style="margin: 0 auto;">
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<img src="./figures/swebench.png" style="width: 100%;" />
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- **Compact Filtering** **(Us):** Inspired by DAPO, we mask the loss for trajectories that reach max context length, timeout during generation (20 minutes), or reach maximum steps.
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- **No Entropy Loss (Us):** Entropy loss introduces higher instability and eventually leads to exponentially increasing entropy, which collapses training. Provided that the base model’s token-level entropy is within 0.3-1, entropy loss is not needed.
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A more detailed description of the training recipe can be found in our [blog post](
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## Evaluation
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<a href="https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33" target="_blank" style="margin: 2px;">
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<img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://x.com/Agentica_" style="margin: 2px;">
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DeepSWE-Preview is trained on top of Qwen3-32B with thinking mode enabled. With just 200 steps of RL training, SWE-Bench-Verified score increases by ~20%.
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Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33).
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<div style="margin: 0 auto;">
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<img src="./figures/swebench.png" style="width: 100%;" />
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- **Compact Filtering** **(Us):** Inspired by DAPO, we mask the loss for trajectories that reach max context length, timeout during generation (20 minutes), or reach maximum steps.
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- **No Entropy Loss (Us):** Entropy loss introduces higher instability and eventually leads to exponentially increasing entropy, which collapses training. Provided that the base model’s token-level entropy is within 0.3-1, entropy loss is not needed.
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A more detailed description of the training recipe can be found in our [blog post](https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33).
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## Evaluation
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